I want to analyze binary, multinomial, and count outcomes (as well as the occasional continuous one) for clustered data. The more I search the less I know, and so I'm hoping the list can provide me some guidance about which of the many alternatives to choose. The nlme package seemed the obvious place to start. However, it seems to be using specifications from nls, which does non-linear least squares. I found the documentation opaque, and I'd prefer to stay in the generalized linear model framework and, ideally, maximum likelihood estimators. (A recent review found maximum likelihood estimators using quadrature performed better than penalized likelhood methods, which specifically included glmmPQL in MASS: http://www.ncbi.nlm.nih.gov/pubmed/20949128). The lme4 package apparently supports generalized linear models. The title of the package is "lme4: Linear mixed-effects models using S4 classes" but the brief description is "Fit linear and generalized linear mixed-effects models." Various people, including Douglas Bates in 2011 (http://lme4.r-forge.r-project.org/slides/2011-01-11-Madison/5GLMM.pdf) who is an author of both nlme and lme4, seem to use it. Some 2007 slides by Chris Manning: http://nlp.stanford.edu/~manning/courses/ling289/GLMM.pdf also use lme4. However, http://cran.cnr.berkeley.edu/web/views/SocialSciences.html says "the lme4 package, which largely supersedes nlme for *linear* mixed models", suggesting nlme is the most appropriate choice. Finally, there's gee in the same problem area. Since I'm fuzzy on the underlying theory, and actually want to use the models to generate individual level imputations (and I know GEE is about the marginal distributions), I'd also rather avoid it. Thanks for any guidance. Summarizing, the candidates include at least nlme glmmPQL (in MASS) lme4 gee I think lme4 is what I want, despite the title and the Social Science task page. Ross Boylan P.S. Zero inflated models would be nice too.
Mitchell Maltenfort
2013-Feb-22 02:02 UTC
[R] How to do generalized linear mixed effects models
One more link to look at http://glmm.wikidot.com/faq This is the r-sig-mixed-models FAQ. On Thu, Feb 21, 2013 at 8:53 PM, Ross Boylan <ross@biostat.ucsf.edu> wrote:> I want to analyze binary, multinomial, and count outcomes (as well as the > occasional continuous one) for clustered data. > The more I search the less I know, and so I'm hoping the list can provide > me some guidance about which of the many alternatives to choose. > > The nlme package seemed the obvious place to start. However, it seems to > be using specifications from nls, which does non-linear least squares. I > found the documentation opaque, and I'd prefer to stay in the generalized > linear model framework and, ideally, maximum likelihood estimators. (A > recent review found maximum likelihood estimators using quadrature > performed better than penalized likelhood methods, which specifically > included glmmPQL in MASS: http://www.ncbi.nlm.nih.gov/**pubmed/20949128<http://www.ncbi.nlm.nih.gov/pubmed/20949128> > ). > > The lme4 package apparently supports generalized linear models. The title > of the package is "lme4: Linear mixed-effects models using S4 classes" but > the brief description is "Fit linear and generalized linear mixed-effects > models." > > Various people, including Douglas Bates in 2011 (http://lme4.r-forge.r-** > project.org/slides/2011-01-11-**Madison/5GLMM.pdf<http://lme4.r-forge.r-project.org/slides/2011-01-11-Madison/5GLMM.pdf>) > who is an author of both nlme and lme4, seem to use it. Some 2007 slides by > Chris Manning: http://nlp.stanford.edu/~**manning/courses/ling289/GLMM.** > pdf <http://nlp.stanford.edu/~manning/courses/ling289/GLMM.pdf> also use > lme4. > > However, http://cran.cnr.berkeley.edu/**web/views/SocialSciences.html<http://cran.cnr.berkeley.edu/web/views/SocialSciences.html>says "the lme4 package, which largely supersedes nlme for *linear* mixed > models", suggesting nlme is the most appropriate choice. > > Finally, there's gee in the same problem area. Since I'm fuzzy on the > underlying theory, and actually want to use the models to generate > individual level imputations (and I know GEE is about the marginal > distributions), I'd also rather avoid it. > > Thanks for any guidance. Summarizing, the candidates include at least > nlme > glmmPQL (in MASS) > lme4 > gee > > I think lme4 is what I want, despite the title and the Social Science task > page. > > Ross Boylan > > > P.S. Zero inflated models would be nice too. > > ______________________________**________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help> > PLEASE do read the posting guide http://www.R-project.org/** > posting-guide.html <http://www.R-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]